High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory, Zagidullina Aygul

Автор: Giuseppe Da Prato
Название: An Introduction to Infinite-Dimensional Analysis
ISBN: 3642421687 ISBN-13(EAN): 9783642421686
Издательство: Springer
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Цена: 46570.00 T
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Описание: Based on well-known lectures given at Scuola Normale Superiore in Pisa, this book introduces analysis in a separable Hilbert space of infinite dimension. It starts from the definition of Gaussian measures in Hilbert spaces, concepts such as the Cameron-Martin formula, Brownian motion and Wiener integral are introduced in a simple way.

Автор: Wilcox, Rand R. (university Of Southern California, Usa)
Название: Introduction to robust estimation and hypothesis testing
ISBN: 0128200987 ISBN-13(EAN): 9780128200988
Издательство: Elsevier Science
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Цена: 110030.00 T
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Описание: Follow one girl as she builds a rocket and plans to take her friends on an amazing trip to the Sun and Moon. But will the task prove more difficult than she first thought? Imaginatively illustrated by T.S Spookytooth, this clever and inventive poem was written by eleven-year-old Collins Big Cat 2011 Writing Competition winner Nicole Sharrocks.

Автор: Lewis, Frank L.
Название: Optimal and Robust Estimation
ISBN: 0849390087 ISBN-13(EAN): 9780849390081
Издательство: Taylor&Francis
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Цена: 158230.00 T
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Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 1441927093 ISBN-13(EAN): 9781441927095
Издательство: Springer
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Цена: 102480.00 T
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Описание: Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Название: Introduction to Small Area Estimation Techniques: A Practical Guide for National Statistics Offices
ISBN: 9292622226 ISBN-13(EAN): 9789292622220
Издательство: Mare Nostrum (Eurospan)
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Цена: 22570.00 T
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Описание: This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways.

It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.

Автор: Giraud Christophe
Название: Introduction to High-Dimensional Statistics
ISBN: 0367716224 ISBN-13(EAN): 9780367716226
Издательство: Taylor&Francis
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Цена: 83690.00 T
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Описание: This book preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities.

Автор: Giraud
Название: Introduction to High-Dimensional Statistics
ISBN: 1482237946 ISBN-13(EAN): 9781482237948
Издательство: Taylor&Francis
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Цена: 64300.00 T
Наличие на складе: Нет в наличии.
Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.

Автор: Eric Gautier and Pierre Alquier
Название: Inverse problems and high-dimensional estimation
ISBN: 3642199887 ISBN-13(EAN): 9783642199882
Издательство: Springer
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Цена: 102480.00 T
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Описание: The product of a high-flying summer school in Paris in 2009, this volume synthesises the state of the art on ill-posed statistical inverse problems and high-dimensional estimation and explores the ways these techniques can be applied to economics.

Автор: Adachi Kohei
Название: Matrix-Based Introduction to Multivariate Data Analysis
ISBN: 9811095957 ISBN-13(EAN): 9789811095955
Издательство: Springer
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Цена: 74530.00 T
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Описание: Part 1. Elementary Statistics with Matrices.- 1 Introduction to Matrix Operations.- 2 Intra-variable Statistics.- 3 Inter-variable Statistics.- Part 2. Least Squares Procedures.- 4 Regression Analysis.- 5 Principal Component Analysis (Part 1).- 6 Principal Component Analysis 2 (Part 2).- 7 Cluster Analysis.- Part 3. Maximum Likelihood Procedures.- 8 Maximum Likelihood and Normal Distributions.- 9 Path Analysis.- 10 Confirmatory Factor Analysis.- 11 Structural Equation Modeling.- 12 Exploratory Factor Analysis.- Part 4. Miscellaneous Procedures.- 13 Rotation Techniques.- 14 Canonical Correlation and Multiple Correspondence Analyses.- 15 Discriminant Analysis.- 16 Multidimensional Scaling.- Appendices.- A1 Geometric Understanding of Matrices and Vectors.- A2 Decomposition of Sums of Squares.- A3 Singular Value Decomposition (SVD).- A4 Matrix Computation Using SVD.- A5 Supplements for Probability Densities and Likelihoods.- A6 Iterative Algorithms.- References.- Index.

Автор: Adachi Kohei
Название: Matrix-Based Introduction to Multivariate Data Analysis
ISBN: 9811541051 ISBN-13(EAN): 9789811541056
Издательство: Springer
Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.

Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.

The book begins by explaining fundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra.

Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.

Автор: Adachi Kohei
Название: Matrix-Based Introduction to Multivariate Data Analysis
ISBN: 9811541027 ISBN-13(EAN): 9789811541025
Издательство: Springer
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Цена: 102480.00 T
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Описание:

Elementary matrix operations.- Intravariable statistics.- Inter-variable statistics.- Regression analysis.- Principal component analysis.- Principal component.

Автор: Pourahmadi Mohsen
Название: High-dimensional Covariance Estimation
ISBN: 1118034295 ISBN-13(EAN): 9781118034293
Издательство: Wiley
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Цена: 84430.00 T
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Описание: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences.